Wide Computation: A Mechanistic Account

Dissertation, University of Edinburgh (2020)
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Abstract

This Ph.D. thesis explores a novel way of thinking about computation in cognitive science. It argues for what I call ‘the mechanistic account of wide computationalism’, or simply wide mechanistic computation. The key claim is that some cognitive and perceptual abilities are produced by or are the result of computational mechanisms that are, in part, located outside the individual; that computational systems, the ones that form the proper units of analysis in cognitive science, are particular types of functional mechanisms that, on occasion, spread out across brain, body, and world. Wide mechanistic computation is the result of bringing together two distinct strands of thinking about computation: ‘wide’ views, which hold that computational systems can, on occasion, include parts of the surrounding environment; and ‘mechanistic’ views, which hold that computational explanation is a species of mechanistic explanation, and that computational mechanisms are a special type of functional mechanism. I argue that wide mechanistic computation draws support from several sources. First, I examine research on animal and human psychology and show that several organisms’ behaviours are properly treated as being the output of wide computational mechanisms. Second, I defend the view from several philosophical charges, including worries about its explanatory parsimony and empirical testability. Finally, I argue for the view’s theoretical credentials by showing that it can help resolve specific problems that have recently troubled 4E cognition. The result is an argument for not only the coherence but also empirical plausibility of wide mechanistic computation. On route to its main objective, the thesis also accomplishes a number of related tasks, including: providing a framework for organising and conceptualising different views of computation, securing the conceptual foundations of mechanistic computation by addressing an outstanding challenge called the ‘abstraction problem’, sounding a cautionary note about recent predictive processing accounts of extended cognition and arguing against a particular conception of levels often used within cognitive science, what is labelled the ‘hierarchical correspondence view of levels’.

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Luke Kersten
University of Alberta

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